Presenting logistic regression-based landslide susceptibility results

Space-time landslide predictive modelling

This chapter deals with types, purposes, contents and perspectives of geomorphological mapping, from the widely applied, ‘traditional’ symbol-based maps to the modern geographical information system (GIS)-based, object-oriented geomorphological mapping models, capable of providing reliable multiscale cartographic support to environmental analysis and land planning projects. In the last decades, the availability of new tools such as satellite imagery, global positioning systems, digital elevation models and GIS has induced a new, more effective approach in the acquisition, storage and display of geomorphological features. By this approach, geomorphologists can produce geomorphological models, consisting of land surface ‘objects’, organised into hierarchically arranged classes with spatially and temporally variable properties and geometric relationships. Unlike ‘traditional’ maps, object-oriented models can provide a full geometric description of landforms and near-surface deposits. Moreover, generalisation/decomposition procedures allow the translation of geomorphological objects from larger to smaller scales and vice versa. A case study of a GIS-based, object-oriented geomorphological model, in which ‘traditional’ symbol-oriented mapping is associated with grid-based/object-based analysis, is provided by the Salerno University (Italy) GeoMorphological Information System (GmIS). Some significant examples of traditional geomorphological maps and multiscale object-oriented mapping models produced at the Salerno University are presented in the annexes.

Nature and Aims of Geomorphological Mapping

Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment. Investigators have for long attempted to estimate landslide hazard to determine where, when, and how destructive landslides are expected to be in an area. This information is useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most attempts at predicting the occurrence of populations of landslides rely on the observation that landslides are the result of multiple interacting, conditioning and triggering factors. Here, we propose a novel Bayesian modelling framework for the prediction of space-time landslide occurrences of the slide type caused by weather triggers. We consider log-Gaussian cox processes, assuming that individual landslides stem from a point process described by an unknown intensity function. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory spanning 1941-2014 is available together with lithological and bedding data. We tested five models of increasing complexity. Our most complex model includes fixed effects and latent spatio-temporal effects, thus largely fulfilling the common definition of landslide hazard in the literature. We quantified the spatio-temporal predictive skill of our model and found that it performed better than simpler alternatives. We then developed a novel classification strategy and prepared an intensity-susceptibility landslide map, providing more information than traditional susceptibility zonations for land planning and management. We expect our novel approach to lead to better projections of future landslides, and to improve our collective understanding of the evolution of landscapes dominated by mass-wasting processes under geophysical and weather triggers.

/pdf/space-time-landslide-predictive-modelling-11b5so0rly.pdf

Space-Time Landslide Predictive Modelling

In this paper, we investigate earthquake-induced landslides using a geostatistical model that includes a latent spatial effect (LSE). The LSE represents the spatially structured residuals in the data, which are complementary to the information carried by the covariates. To determine whether the LSE can capture the residual signal from a given trigger, we test whether the LSE is able to capture the pattern of seismic shaking caused by an earthquake from the distribution of seismically induced landslides, without prior knowledge of the earthquake being included in the statistical model. We assess the landslide intensity, i.e., the expected number of landslide activations per mapping unit, for the area in which landslides triggered by the Wenchuan (M 7.9, May 12, 2008) and Lushan (M 6.6, April 20, 2013) earthquakes overlap. We chose an area of overlapping landslides in order to test our method on landslide inventories located in the near and far fields of the earthquake. We generated three different models for each earthquake-induced landslide scenario: i) seismic parameters only (as a proxy for the trigger); ii) the LSE only; and iii) both seismic parameters and the LSE. The three configurations share the same set of morphometric covariates. This allowed us to study the pattern in the LSE and assess whether it adequately approximated the effects of seismic wave propagation. Moreover, it allowed us to check whether the LSE captured effects that are not explained by the shaking levels, such as topographic amplification. Our results show that the LSE reproduced the shaking patterns in space for both earthquakes with a level of spatial detail even greater than the seismic parameters. In addition, the models including the LSE perform better than conventional models featuring seismic parameters only.

Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides

Please click here to download the map associated with this article. This paper illustrates a geographic information system (GIS) supported methodology for the assessment of landslide susceptibility. The methodology involves four operational steps: survey, site analysis, macroarea analysis and susceptibility analysis. The Survey includes the production (or acquisition) of a large-scale litho-technical map, a large-scale geomorphological map, a detailed inventory of past and present landslide events, and a high resolution DTM (Digital Terrain Model. Site analysis leads to the definition of discriminating parameters (commonly, lithological and morphometric conditions necessary but not sufficient to trigger a landslide of a given type) and predisposing factors (conditions that worsen slope stability but are not sufficient to trigger a landslide of a given type in the absence of discriminating parameters). The different predisposing factors are subdivided into classes, whose intervals are established ...

GIS Methodology to Assess Landslide Susceptibility: Application to a River Catchment of Central Italy

GIS methodology to assess landslide susceptibility: application to a river catchment of central Italy This paper illustrates a GIS supported methodology for the assessment of landslide susceptibility. The methodology involves four steps: survey, site analysis, macro-area analysis, and susceptibility analysis. Statiscal and GIS processing of basical large scale geological dataset leads to the recognition of discriminating parameters (land conditions necessary but not sufficient to trigger landslides) and predisposing factors (conditions that worsen slope stability) separately for each landslides types. The susceptibility function combines GIS data to draw landslide susceptibility maps. These results represent the preliminary step for the assessment of landslide hazard and risk.

/pdf/una-metodologia-gis-per-la-valutazione-della-suscettibilita-303lo72mr4.pdf

Una metodologia GIS per la valutazione della suscettibilità da frana

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GIS Methodology to Assess Landslide Susceptibility: Application to a River Catchment of Central Italy

Una metodologia GIS per la valutazione della suscettibilità da frana